1. Field of the Invention
The present invention generally relates to an apparatus and a method for generating driver assistance information of a traveling vehicle, and more particularly to an apparatus and a method for generating driver assistance information of a traveling vehicle so a driver can easily recognize the road conditions and the positions of adjacent vehicles in all directions of a traveling vehicle through lane recognition and vehicle detection.
2. Description of the Related Art
When a vehicle is traveling, rear-end accidents frequently occur due to careless driving, poor visibility, etc. That is, traffic accidents caused by lane deviation and rear-end accidents with a traveling vehicle can occur due to poor visibility, insufficient concentration of a driver, etc., in long distance driving, when it rains or in night driving. Such traffic accidents generally occur due to carelessness of a driver. If a driver can estimate such situations 2 to 3 seconds in advance and properly cope with the situations, such accidents can be reduced by more than 50% or may be minor accidents. In order to prevent such rear-ending, a driver assistance system has been provided, which can become aware of inter-vehicle position and speed through vehicle detection, and can provide information, such as an inter-vehicle collision warning, collision avoidance, cruise control, etc. Such a driver assistance system is capable of providing traveling information or a danger warning, and preventing safety accidents through active involvement so a driver can drive a vehicle more conveniently and safely. For such a driver assistance system, an assistance information generation system including a rear parking warning system, a lane deviation warning system, a drowsy driving warning system, etc., has been researched together with a lane deviation prevention system, an intelligent cruise control system, etc., which can perform active steering or speed control.
In order to extract lane or adjacent vehicle information, a conventional driver assistance system, as described above, has used various methods for detecting lanes in front from a camera, for detecting vehicles in front from a camera, for detecting vehicles in front by using radar, for detecting vehicles to the side/rear by using a camera installed at a side-mirror for rear monitoring, for detecting vehicles to the side by using a side ultrasonic sensor, etc. The extracted information about primary lanes and vehicles is used for generating valid information transmittable to a driver through various methods for estimating the relative position of a traveling vehicle and a lane, for estimating a lane deviation time point, for estimating a relative distance and a relative speed with an adjacent vehicle, and for estimating inter-vehicle collision by using the estimated information. Further, a warning sound, a warning lamp, vibration of a handle or a driver's seat, etc., has been used as an interface for reporting the extracted valid information to a driver. In addition to the means for reporting dangerous situations, a method for continuing to display traveling situations has used various methods for displaying the position and distance of a traveling vehicle and a vehicle in front on a straight road, for displaying vehicle information on road shape information extracted through sensor processing including a camera, radar and laser, for overlappingly displaying the position and distance information of a vehicle extracted through road information generated from a map database, etc.
A conventional driver assistance system, as described above, has required precise environment recognition and proper information transfer, but has mainly monitored only specific information about lane deviation or inter-vehicle collision situations in the front or side/rear of a vehicle, and has transferred only limited information to a driver. Therefore, such a system has inherent deficiencies due, for example, to possible obstructions in the vehicle. For environment recognition in all directions, a proper sensor arrangement is required to remove blind spots of a vehicle by using a minimum number of sensors, and front, rear, right/left side information must be exchanged and integrated instead of simply being combined. There are limitations in transferring synthetic information about all directions, such as front/rear/right/left lane criteria, corresponding vehicle criteria, distance, speed, collision or non-collision with adjacent vehicles, etc., to a driver mainly monitoring only front situations through a conventional simple interface.
Since the prior art displays traveling vehicle status and road conditions, and also extracts only the shape of a road through curvature information regardless of extracting road information, it is impossible to become aware of information about right/left side lanes. That is, conventional lane recognition technology, i.e. technology regarding lane recognition from images, extracts lane parts by using a brightness difference of a lane and a road, and computes the slope of the lane through inverse-perspective processing to calculate the curvature of the lane, or extracts lane parts through edge detection by a mask, and computes respective slopes in short and long distances to calculate the curvature of the lane. While a variety of distances may arbitrarily be considered as long and short distances, as used herein, a short distance is considered to be less than or equal to a preset distance, such as twenty meters or the like, and a long distance is considered to be greater than the preset distance. Further, the technology estimates the shape of the lane and the position of a vehicle for the lane through such lane recognition, and warns of lane deviation through a voice. However, since such a lane recognition method does not adaptively cope with changes in images or brightness of a lane, and thus does not easily extract a lane candidate, the entire performance of a lane recognition system may deteriorate, and significant time is required to perform inverse-perspective processing for images.
A road with a curvature is seen as a straight line with a constant slope and a curve with a small curvature in a short distance due to perspective effect, but it has a large curvature in a long distance. Further, in a long distance, lane candidates are insufficient based on lane criteria as compared to a short distance. Therefore, it is difficult to exactly estimate a curvature by estimating the entire curvature of a lane only with short distance information, or detecting lane candidates in a long distance by using a preset curvature value and computing a curvature by using this. In order to exactly estimate the curvature of a lane, it is necessary to provide a method capable of finding the exact position of a lane in a short distance, computing the curvature of a lane candidate located in a long distance, comparing this with the position of a short distance lane, and exactly finding a lane located in a long distance as well as a short distance.
When a vehicle moves onto a lane or a lane is worn or obscured due to antiquation of a road, it is necessary to provide a method capable of estimating a lane based on information about an obvious lane and lane width information. In addition, it is necessary to provide a process for stipulating a relation between a lane and a camera and thus exactly computing a position relation between the land and a vehicle, instead of lane detection. A lane deviation time point can be estimated based on the shape of a lane and a position relation between a lane and a vehicle. However, when the vehicle deviates from a central line, or moves toward a roadside, the possibility of safety accidents normally increases. On account of this, it is necessary to distinguish a dot lane from a solid lane, and noise elimination using integration of front/rear lane recognition is required for coping with a case where road conditions are different as with backlight or an intersection.
In the meantime, in order to generate road information by using a map database, a position measurement means should be precise, but is normally only available for roads for which map databases have been established. It is difficult to apply a position measurement means to a driver assistance system using position and direction information instead of an approximate position relation of a lane and a vehicle. Further, since conventional driver assistance systems monitor only the front of a traveling vehicle, it is difficult to overcome the limitations of such systems.